This book offers a comprehensive analysis of the significant intersection where human factors and cyber-physical system (CPS) reliability meet. Physical component integration has become essential in a number of industries, including smart infrastructure, health care, transportation, and manufacturing. However, human performance and decision-making inside these complex frameworks also play an important part in determining the reliability of CPS. Key subjects discussed include the role of human factors in CPS reliability, machine learning and deep learning applications in cybersecurity, resilience engineering, cognitive task management, efficient team collaboration and communication, error control, and cybersecurity awareness.

The book will be read by professionals and scholars working in engineering, human factors, reliability engineering, cybersecurity, and related topics. In order to obtain a greater knowledge of the crucial role that human factors play in providing the reliability and trustworthiness of CPS, it is also beneficial for students pursuing courses or research in CPS, humancomputer interface (HCI), and systems engineering.

Integrates human factors into Cyber-Physical Systems (CPS) reliability Balances theory with actionable tools for CPS enhancement Includes applications, real world, case studies, and best practices

Autorentext

Dr. Gururaj H L (Senior Member, IEEE) received a Ph.D. degree in computer science and engineering from Visvesvaraya Technological University India in 2019. He has published more than 200 research articles in peer-reviewed and reputed international journals. He has authored 15 edited books in Springer, IET, IGI Global, and Taylor & Francis.  He is Senior Member of ACM. He received Young Scientist International Travel Support ITS-SERB, Department of Science and Technology, Government of India, in December 2016. He was appointed as ACM Distinguish Speaker (2018–2021) by the ACM U.S. Council. He has honored as Keynote Speaker, Session Chair, TPC Member, Advisory Committee Member at international seminars, workshops, and conferences across globe. Prof. Gururaj’s research interests are applications in machine and federated learning, data mining, blockchain, and cyber security.

Vinayakumar Ravi is Assistant Research Professor at Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia. His current research interests include applications of data mining, artificial intelligence, machine learning (including deep learning) for biomedical informatics, cyber security, image processing, and natural language processing. He has more than 100 research publications in reputed IEEE conferences, IEEE transactions, and journals. His publications include prestigious conferences in the area of cyber security, like IEEE S&P and IEEE Infocom. Dr. Ravi has received a full scholarship to attend Machine Learning Summer School (MLSS) 2019, London. He has organized a shared task on detecting malicious domain names (DMD 2018) as part of SSCC'18 and ICACCI'18. He received the Chancellor's Research Excellence Award in AIRA 2021, and his name was included in the World's Top 2% Scientists by Stanford University published in PLoS Biology.

Hoang Pham is Distinguished Professor and Former Chairman (2007–2013) of the Department of Industrial and Systems Engineering at Rutgers University. His research areas include reliability modeling and prediction, software reliability, and statistical inference. He is Editor-in-Chief of the International Journal of Reliability, Quality, and Safety Engineering and Editor of Springer Series in Reliability Engineering and has been Conference Chair and Program Chair of over 50 international conferences and workshops. Dr. Pham is Author or Coauthor of seven books and has published over 220 journal articles, 100 conference papers, and edited 17 books including Springer Handbook in Engineering Statistics and Handbook in Reliability Engineering. He has delivered over 50 invited keynote and plenary speeches at many international conferences and institutions. His numerous awards include the 2009 IEEE Reliability Society Engineer of the Year Award. He is Fellow of the IEEE, AAIA, and IISE.

Dayananda P (Senior Member, IEEE) received the M.Tech. degree from RVCE and the Ph.D. degree from VTU. He is currently Professor in information technology with the Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education. His research interests include image processing and information retrieval. He has published many papers in national and international journals in the field of image processing and retrieval. He has got few research grants and consultancy into his account.

Inhalt

Recurrent integrated CNN gate (RICG): A dynamic deep learning model for security and efficiency enhancement in cyber-physical systems.- A cognitive workload-aware machine learning model for performance enhancement in cyber-physical systems.- Audio driven detection of hate speech in Telugu: Toward ethical and secure CPS.- Vision transformer-based audio analysis for depression detection: A human factor in reliable CPS.- A distributed approach based on Catboost, BlockChain and edge computing for IoT security.- An improved anomaly detection based on ensemble learning and deep Q-network for mobile edge computing monitoring.- Efficient anomaly detection for cyber-physical leveraging knowledge distillation and model quantization.- Interpretable anomaly detection for cyber-physical system risk mitigation using CNN and SHAP.- Intrusion detection approaches in healthcare systems: An overview.- Optimizing intrusion detection systems: A machine learning-based feature selection approach for enhanced cybersecurity.- Human factors in cyber-physical systems: Bridging the gap between humans and technology.- Ensemble-based cognitive IDS for IIoT in cyber-physical environments.- Towards reliable and secure IoMT: A deep learning perspective on cyber-physical threats.- Improved computational diffie-hellman-based mechanism for cyber-physical security.- Enhancing PE malware detection: A comparative study of feature-based and image-based representations.- A lightweight attention-enhanced deep learning framework for malware detection in IoT: A comparative study of structured and image-based data representations.- Machine learning for cyber-physical systems: A short survey.- Formal methods for cyber-physical systems.